150 lines
5.5 KiB
Markdown
150 lines
5.5 KiB
Markdown
# Forecast the usage of a GitLab namespace
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In this lab we will use the `forecast` command to forecast potential GitHub Actions usage by computing metrics from historical pipeline data from the GitLab instance. The metrics will be stored on disk in a markdown file and include job metrics for execution time, queue time, and concurrency. We will look at each of these metrics in more depth later in this lab.
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- [Prerequisites](#prerequisites)
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- [Prepare for forecast](#prepare-for-forecast)
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- [Perform a forecast](#perform-a-forecast)
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- [Review forecast report](#review-forecast-report)
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## Prerequisites
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1. Followed [steps](../gitlab#readme) to set up your codespace environment.
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2. Completed the [configure lab](../gitlab/valet-configure-lab.md).
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3. Ran the setup script in the terminal to make sure the GitLab instance is ready .
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```
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source gitlab/bootstrap/setup.sh
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```
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## Prepare for forecast
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Before we can run the forecast we need to answer a few questions so we can construct the correct command.
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1) What namespace do we want to run the forecast for? __valet. This is the only group in the demo GitLab instance.__
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2) What is the date we want to start forecasting from? __08-02-2022. This date is before the time the data was populated on our demo GitLab instance. In practice, this should be a date that will give you enough data to get a good understand of the typical usage. Too little data and the metrics might not give a accurrate picture__
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3) Where do we want to store the results? __./tmp/forecast_reports. This can be any valid path on the system, but for simplicity it is recommend to use a directory in the root of the codespace workspace.__
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## Perform a forecast
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- Construct the command using the values from the questions above, it should look like:
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```
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gh valet forecast gitlab --output-dir ./tmp/forecast_reports --namespace valet --start-date 08-02-2022
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```
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- Run the command in the codespace terminal.
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- Verify that the command output is similar to this.
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## Review forecast report
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Open the forecast report and review the calculated metrics.
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- From the codespace explorer pane find `./tmp/forecast_reports/forecast_report.md` and right-click, and select __Open Preview__.
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- The file should be similar to this.
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<details>
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<summary>example forecast_report.md</summary>
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# Forecast report for [GitLab](http://localhost/valet)
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- Valet version: **0.1.0.13432(03b5bc9370a8f0073c0cc1a4b25f6b81d0005c0f)**
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- Performed at: **8/17/22 at 20:00**
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- Date range: **2/8/22 - 8/17/22**
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## Total
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- Job count: **57**
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- Pipeline count: **15**
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- Execution time
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- Total: **135 minutes**
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- Median: **0 minutes**
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- P90: **7 minutes**
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- Min: **0 minutes**
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- Max: **10 minutes**
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- Queue time
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- Median: **0 minutes**
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- P90: **5 minutes**
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- Min: **0 minutes**
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- Max: **42 minutes**
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- Concurrent jobs
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- Median: **0**
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- P90: **0**
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- Min: **0**
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- Max: **9**
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---
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## gitlab-runner
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- Job count: **57**
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- Pipeline count: **15**
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- Execution time
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- Total: **135 minutes**
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- Median: **0 minutes**
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- P90: **7 minutes**
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- Min: **0 minutes**
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- Max: **10 minutes**
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- Queue time
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- Median: **0 minutes**
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- P90: **5 minutes**
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- Min: **0 minutes**
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- Max: **42 minutes**
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- Concurrent jobs
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- Median: **0**
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- P90: **0**
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- Min: **0**
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- Max: **9**
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> Note: Concurrent jobs are calculated by using a sliding window of 1m 0s.
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</details>
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### Metric Definitions
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| Name | Description |
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| ----- | ----------- |
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| Median | The __middle__ value |
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| P90 | 90% of the values are less than or equal too |
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| Min | The lowest value |
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| Max | The highest value |
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### Total Section
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- This section shows the metrics for all of the jobs run in projects contained in the `valet` group, from 08/02/2022 to the time the command was executed.
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## Total
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- Job count: **57**
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- Pipeline count: **15**
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---
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We can see we ran 15 pipelines that contained 57 jobs. The number of jobs is expected to be larger than pipelines because a pipeline is typically a collection of jobs. For example `basic-pipeline-example` contains 6 jobs
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- `Execution time` shows the metrics for the time a job __took to run__. Looking closer we can see during our forecast timeframe the total job run time was 135 minutes with 90% of the jobs finishing under 7 minutes, and the longest job taking 10 minutes. The `min` is 0 because the quick job took less than a minute and was rounded down to 0.
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- Execution time
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- Total: **135 minutes**
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- Median: **0 minutes**
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- P90: **7 minutes**
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- Min: **0 minutes**
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- Max: **10 minutes**
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- `Queue time` shows the metrics for how long jobs __waited__ for a runner to be available.
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- Queue time
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- Median: **0 minutes**
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- P90: **5 minutes**
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- Min: **0 minutes**
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- Max: **42 minutes**
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- `Concurent jobs` show the metrics for how many jobs were run at the __same time__.
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- Concurrent jobs
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- Median: **0**
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- P90: **0**
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- Min: **0**
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- Max: **9**
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### Runner Group Sections
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- The preceeding section show the same metrics as the `Total` section
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### TBD
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